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Faster and Cheaper Serverless computing on Harvested Resources 21

Faster and Cheaper Serverless Computing on Harvested Resourc...

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28th acm symposium on Operating Systems principles (SOSP)

作者： Zhang, Yanqi Goiri, Inigo Chaudhry, Gohar Irfan Fonseca, Rodrigo Elnikety, Sameh Delimitrou, Christina Bianchini, Ricardo Cornell Univ Ithaca NY 14853 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781450387095

Serverless computing is becoming increasingly popular due to its ease of programming, fast elasticity, and fine-grained billing. However, the serverless provider still needs to provision, manage, and pay the IaaS provider for the virtual machines (VMs) hosting its platform. This ties the cost of the serverless platform to the cost of the underlying VMs. One way to significantly reduce cost is to use spare resources, which cloud providers rent at a massive discount. Harvest VMs offer such cheap resources: they grow and shrink to harvest all the unallocated CPU cores in their host servers, but may be evicted to make room for more expensive VMs. Thus, using Harvest VMs to run the serverless platform comes with two main challenges that must be carefully managed: VM evictions and dynamically varying resources in each VM. In this work, we explore the challenges and benefits of hosting serverless (Function as a Service or simply FaaS) platforms on Harvest VMs. We characterize the serverless workloads and Harvest VMs of Microsoft Azure, and design a serverless load balancer that is aware of evictions and resource variations in Harvest VMs. We modify OpenWhisk, a widely-used open-source serverless platform, to monitor harvested resources and balance the load accordingly, and evaluate it experimentally. Our results show that adopting harvested resources improves efficiency and reduces cost. Under the same cost budget, running serverless platforms on harvested resources achieves 2.2x to 9.0x higher throughput compared to using dedicated resources. When using the same amount of resources, running serverless platforms on harvested resources achieves 48% to 89% cost savings with lower latency due to better load balancing.

关键词： Serverless computing harvested resources

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A Deterministic Algorithm for the MST Problem in Constant Rounds of Congested Clique 2021

A Deterministic Algorithm for the MST Problem in Constant Ro...

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53rd Annual acm SIGACT symposium on Theory of computing (STOC)

作者： Nowicki, Krzysztof Univ Copenhagen Copenhagen Denmark Univ Wroclaw Wroclaw Poland

ISBN: (纸本)9781450380539

In this paper we show that the Minimum Spanning Tree problem (MST) can be solved deterministically in O(1) rounds of the Congested Clique model. In the Congested Clique model there are n players that perform computation in synchronous rounds. Each round consist of a phase of local computation and a phase of communication, in which each pair of players is allowed to exchange O(log n) bit messages. The studies of this model began with the MST problem: in the paper by Lotker, Pavlov, Patt-Shamir, and Peleg [SPAA'03, SICOMP'05] that defines the Congested Clique model the authors give a deterministic O(log log n) round algorithm that improved over a trivial O(log n) round adaptation of Boravka's algorithm. There was a sequence of gradual improvements to this result: an O(log log log n) round algorithm by Hegeman, Pandurangan, Pemmaraju, Sardeshmukh, and Scquizzato [PODC'15], an O(log* n) round algorithm by Ghaffari and Parter, [PODC'16] and an O(1) round algorithm by Jurdzinski and Nowicki, [SODA'18], but all those algorithms were randomized. Therefore, the question about the existence of any deterministic o(log log n) round algorithms for the Minimum Spanning Tree problem remains open since the seminal paper by Lotker, Pavlov, Patt-Shamir, and Peleg [SPAA'03, SICOMP'05]. Our result resolves this question and establishes that O(1) rounds is enough to solve the MST problem in the Congested CI iq ue model, even if we are not allowed to use any randomness. Furthermore, the amount of communication needed by the algorithm makes it applicable to a variant of the M PC model using machines with local memory of size O(n).

关键词： Minimum Spanning Tree MST Deterministic Algorithms Graph algorithms distributed Algorithms Congested Clique Parallel Algorithms Massively Parallel Algorithms MapReduce

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SHOWAR: Right-Sizing And Efficient Scheduling of Microservices 12

<i>SHOWAR</i>: Right-Sizing And Efficient Scheduling of Micr...

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12th Annual acm symposium on Cloud computing (SoCC)

作者： Baarzi, Ataollah Fatahi Kesidis, George Penn State Univ University Pk PA 16802 USA

ISBN: (纸本)9781450386388

Microservices architecture have been widely adopted in designing distributed cloud applications where the application is decoupled into multiple small components (i.e. "microservices"). One of the challenges in deploying microservices is finding the optimal amount of resources (i.e. size) and the number of instances (i.e. replicas) for each microservice in order to maintain a good performance as well as prevent resource wastage and under-utilization which is not cost-effective. This paper presents SHOWAR, a framework that configures the resources by determining the number of replicas (horizontal scaling) and the amount of CPU and Memory for each microservice (vertical scaling). For vertical scaling, SHOWAR uses empirical variance in the historical resource usage to find the optimal size and mitigate resource wastage. For horizontal scaling, SHOWAR uses basic ideas from control theory along with kernel level performance metrics. Additionally, once the size for each microservice is found, SHOWAR bridges the gap between optimal resource allocation and scheduling by generating affinity rules (i.e. hints) for the scheduler to further improve the performance. Our experiments, using a variety of microservice applications and real-world workloads, show that, compared to the state-of-the-art autoscaling and scheduling systems, SHOWAR on average improves the resource allocation by up to 22% while improving the 99th percentile end-to-end user request latency by 20%.

关键词： autoscaling microservices cloud computing

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An IoT-oriented Multiple Data Replicas Placement Strategy in Hybrid Fog-Cloud Environment 21

An IoT-oriented Multiple Data Replicas Placement Strategy in...

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2021 acm SIGSIM Conference on principles of Advanced Discrete Simulation, SIGSIM-PADS 2021

作者： Ben Salah, Noura Bellamine Ben Saoud, Narjès National School of Computer Science Manouba Tunisia

ISBN: (纸本)9781450382960

The growing adoption of Fog computing for the sensitive-time IoT applications allows to facilitate the real-time actions and to enhance their efficiency and performance. In fact, keeping the data in the distributed Fog network brings the advantages and power of the Cloud closer to where data are generated while saving network bandwidth and reducing latency and operational costs. However, due to the diversity of the Fog nodes, IoT system distribution and data sharing, how and where to place the produced data with low latency is a main challenge. Moreover, a data placement based on a single replica cannot meet the data access requirements of all data consumers that have different topology positions. Thus, in this paper, we propose a multi-objective optimization data placement model in a hybrid Fog-Cloud environment based on multiple data replicas. It aims to find better distributed data storage while optimizing the overall system latency and the used storage space by minimizing the data replicas and following full and partial data replication methods. Further, we propose a greedy algorithm $iFogDP_h$ which uses a refined method to find a solution for assigning the IoT data to the appropriate data hosts in polynomial time by reducing the time required to transfer data for storage, access and replication. We conducted the experiments on iFogSim, a toolkit for modeling and simulation of Fog environments. The experimental results show the effectiveness of our proposed solution in terms of latency, storage overhead and the number of data replicas compared to the existing strategies. © 2021 acm.

关键词： Fog computing

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Igniting Curiosity with BJC Sparks: A Transformative Curriculum for Middle and High School Computer Science 2024

Igniting Curiosity with BJC Sparks: A Transformative Curricu...

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proceedings of the 55th acm Technical symposium on Computer Science Education V. 2

作者： Daniel Garcia Mary Fries Michael Ball Lauren Mock University of California Berkeley Berkeley CA USA Education Development Center Waltham MA USA

ISBN: (纸本)9798400704246

The Beauty and Joy of computing (BJC) Sparks curriculum is an innovative computer science curriculum designed to introduce middle school and early high school students to the world of computer science through functional programming. Developed by the creators of BJC CS principles (CSP), BJC Sparks presents a simplified yet engaging approach to programming, emphasizing the flow of information through functions. By focusing on functions rather than commands and iteration, BJC Sparks nurtures problem-solving skills and reduces bugs in future programming *** SIGCSE 2023, the BJC Sparks team shared their insights into the curriculum's development and implementation, featuring real-world accounts from pioneering teachers. The use of microworlds and multimedia elements captured middle school students' interest, particularly in physical computing micro:bit activities. This approach showcased the potential of multimedia and data science projects in teaching functional programming through exciting, personalized *** on the success of previous summer PD workshops with 76 educators across the U.S., we propose a condensed version of our comprehensive workshop for SIGCSE. Participants will gain a deep understanding of BJC Sparks' design principles, hands-on experience with the Snap! programming language, collaborative lab activities, alignment with CSTA standards, access to teacher materials and resources, and effective pedagogical strategies. Attendees will receive a three-hour professional development certificate and will be equipped to empower the next generation of young minds in the world of functional programming and beyond.

关键词： computer science principles computing education curriculum data science high school introductory programming middle school professional development

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Univariate vs multivariate prediction for containerised applications auto-scaling: a comparative study 25

Univariate vs multivariate prediction for containerised appl...

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40th Annual acm symposium on Applied computing, SAC 2025

作者： Santos, Wellison R. M. Sampaio, Adalberto R. Rosa, Nelson S. Cavalcanti, George D. C. Universidade Federal de Pernambuco Pernambuco Recife Brazil SENAI Institute of Innovation for Information and Communication Technologies Pernambuco Recife Brazil Huawei Distributed Scheduling and Data Engine Lab MarkhamON Canada

ISBN: (纸本)9798400706295

Adaptive containerised systems have been developed using the Time Series Forecasting (TSF) technique. TSF analyses historical data patterns to estimate future trends, assuming they will occur again. Identifying future trends allows anticipating problems (e.g., high latency) and acting (e.g., replicating the service) to fix them before they occur. Depending on the number of features (i.e., metrics) used as input for prediction, TSF can be classified as univariate (single feature) or multivariate (two or more features). Despite the popularity of both TSF strategies, a unique strategy is typically implemented, and there is no comparison with the other. However, it is known that no strategy is the best choice for all possible scenarios. This paper presents a comparative study assessing univariate and multivariate proactive auto-scaling of containerised applications. A custom-made multivariate auto-scaling tool called Multivariate Forecasting Tool (MFT) was developed and compared with a production-grade univariate system called Predict Kube (PK). Both applications were evaluated using four popular open-source benchmark applications. The results show that the multivariate strategy decreased the response time of the evaluated applications in 75% of the experiments (i.e., 9 out of 12) compared to the univariate, and it was more cost-effective in half of them (i.e., 6 out of 12). Furthermore, they also indicate that the multivariate strategy efficiency is more significant as the number of containers composing the application increases. This comparative study is expected to be a helpful guide for developers who want to choose the most effective proactive approach for their auto-scaling solutions. Copyright © 2025 held by the owner/author(s).

关键词： time series forecasting

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DLion: Decentralized distributed Deep Learning in Micro-Clouds 21

DLion: Decentralized Distributed Deep Learning in Micro-Clou...

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30th International symposium on High-Performance Parallel and distributed computing (HPDC)

作者： Hong, Rankyung Chandra, Abhishek Univ Minnesota Minneapolis MN 55455 USA

ISBN: (纸本)9781450382175

Deep learning (DL) is a popular technique for building models from large quantities of data such as pictures, videos, messages generated from edges devices at rapid pace all over the world. It is often infeasible to migrate large quantities of data from the edges to centralized data center(s) over WANs for training due to privacy, cost, and performance reasons. At the same time, training large DL models on edge devices is infeasible due to their limited resources. An attractive alternative for DL training distributed data is to use micro-clouds-small-scale clouds deployed near edge devices in multiple locations. However, micro-clouds present the challenges of both computation and network resource heterogeneity as well as dynamism. In this paper, we introduce DLion, a new and generic decentralized distributed DL system designed to address the key challenges in micro-cloud environments, in order to reduce overall training time and improve model accuracy. We present three key techniques in DLion: (1) Weighted dynamic batching to maximize data parallelism for dealing with heterogeneous and dynamic compute capacity, (2) Per-link prioritized gradient exchange to reduce communication overhead for model updates based on available network capacity, and (3) Direct knowledge transfer to improve model accuracy by merging the best performing model parameters. We build a prototype of DLion on top of TensorFlow and show that DLion achieves up to 4.2x speedup in an Amazon GPU cluster, and up to 2x speed up and 26% higher model accuracy in a CPU cluster over four state-of-the-art distributed DL systems.

关键词： Edge computing Deep learning Micro-clouds Resource allocation

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Hardware Specialization for distributed computing 21

Hardware Specialization for Distributed Computing

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30th International symposium on High-Performance Parallel and distributed computing (HPDC)

作者： Alonso, Gustavo Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland

ISBN: (纸本)9781450382175

Several trends in the IT industry are driving an increasing specialization of the hardware layers. On the one hand, demanding workloads, large data volumes, diversity in data types, etc. are all factors contributing to make general purpose computing too inefficient. On the other hand, cloud computing and its economies of scale allow vendors to invest on specialized hardware for particular tasks that otherwise would be too expensive or consume resources needed elsewhere. In this talk I will discuss the shift towards hardware acceleration and show with several examples why specialized systems are here to stay and are likely to dominate the computer landscape for years to come. I will also discuss Enzian, an open research platform developed at ETH to enable the exploration of hardware acceleration and present some preliminary results achieved with it.

关键词： distributed computing Accelerators HPC

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Random Walks on Huge Graphs at Cache Efficiency 21

Random Walks on Huge Graphs at Cache Efficiency

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28th acm symposium on Operating Systems principles (SOSP)

作者： Yang, Ke Ma, Xiaosong Thirumuruganathan, Saravanan Chen, Kang Wu, Yongwei Tsinghua Univ Dept Comp Sci & Technol Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China Hamad Bin Khalifa Univ Qatar Comp Res Inst Ar Rayyan Qatar Beijing HaiZhi XingTu Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9781450387095

Data-intensive applications dominated by random accesses to large working sets fail to utilize the computing power of modern processors. Graph random walk, an indispensable workhorse for many important graph processing and learning applications, is one prominent case of such applications. Existing graph random walk systems are currently unable to match the GPU-side node embedding training speed. This work reveals that existing approaches fail to effectively utilize the modern CPU memory hierarchy, due to the widely held assumption that the inherent randomness in random walks and the skewed nature of graphs render most memory accesses random. We demonstrate that there is actually plenty of spatial and temporal locality to harvest, by careful partitioning, rearranging, and batching of operations. The resulting system, FlashMob, improves both cache and memory bandwidth utilization by making memory accesses more sequential and regular. We also found that a classical combinatorial optimization problem (and its exact pseudo-polynomial solution) can be applied to complex decision making, for accurate yet efficient data/task partitioning. Our comprehensive experiments over diverse graphs show that our system achieves an order of magnitude performance improvement over the fastest existing system. It processes a 58GB real graph at higher per-step speed than the existing system on a 600KB toy graph fitting in the L2 cache.

关键词： graph computing random walk memory cache

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Finding Invariants of distributed Systems: It's a Small (Enough) World After All 18

Finding Invariants of Distributed Systems: It's a Small (Eno...

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18th USENIX symposium on Networked Systems Design and Implementation

作者： Hance, Travis Heule, Marijn Martins, Ruben Parno, Bryan Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781939133212

Today's distributed systems are increasingly complex, leading to subtle bugs that are difficult to detect with standard testing methods. Formal verification can provably rule out such bugs, but historically it has been excessively labor intensive. For distributed systems, recent work shows that, given a correct inductive invariant, nearly all other proof work can be automated;however, the construction of such invariants is still a difficult manual task. In this paper, we demonstrate a new methodology for automating the construction of inductive invariants, given as input a (formal) description of the distributed system and a desired safety condition. Our system performs an exhaustive search within a given space of candidate invariants in order to find and verify inductive invariants which suffice to prove the safety condition. Central to our ability to search efficiently is our algorithm's ability to learn from counterexamples whenever a candidate fails to be invariant, allowing us to check the remaining candidates more efficiently. We hypothesize that many distributed systems, even complex ones, may have concise invariants that make this approach practical, and in support of this, we show that our system is able to identify and verify inductive invariants for the Paxos protocol, which proved too complex for previous work.

关键词： Program debugging

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