Emerging data centers may host a large number of applications that consume CPU power, memory, and I/O resources. Previous studies focus on the allocation of resources in order to perfectly satisfy the demands seen in ...
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With the development of cloud computing, there is a growing number of virtual machines (VMs) in the IaaS cloud. The VM owners can install different kinds of software on demand. However, if the software is not updated ...
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With the development of cloud computing, there is a growing number of virtual machines (VMs) in the IaaS cloud. The VM owners can install different kinds of software on demand. However, if the software is not updated in time, it would be a great threat to the security of the cloud. But for the VM owners, it is a tedious task to keep all of the installed software up to date. In this paper we present a new software update model called UaaS (Update as a Service) to handle the VM (online or offline) update automatically. Multiple VMs share one software update service and multiple update strategies are provided for single software, which can be customized at any time. The ability of UaaS has been evaluated by our experiments, and the results show that UaaS can provide software update service successfully and complete update task for lots of VMs with multiple update strategies efficiently.
Large-scale distributed deep learning is of great importance in various applications. For distributed training, the inter-node gradient communication often becomes the performance bottleneck. Gradient sparsification h...
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With the development of virtualization technology, it¿s desirable to deploy virtual machines to high performance clusters used for data centers. VNIX, developed in servicescomputingtechnology and systemlab, tr...
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With the development of virtualization technology, it¿s desirable to deploy virtual machines to high performance clusters used for data centers. VNIX, developed in servicescomputingtechnology and systemlab, tries to help cluster administrators to manage a large number of virtual machines (VMs) distributed on clusters. To reduce the complexity of virtualization management, VNIX provides a whole-set of tools for monitoring, deploying, controlling, and configuring virtual machines on clusters. In addition to those basic management functions, VNIX also offers a number of specialized tools for clustering VMs. Due to the complex dynamic environment in clusters, it¿s challenging to design such tools. In this paper, we present the design of VNIX, and we describe several use cases of managing VMs in clusters with VNIX. Such use cases illustrate various ways of using VNIX to simplify the management work and to improve resource utilization.
A growing number of applications are moving to serverless architectures for high elasticity and fine-grained billing. For stateful applications, however, the use of serverless architectures is likely to lead to signif...
A growing number of applications are moving to serverless architectures for high elasticity and fine-grained billing. For stateful applications, however, the use of serverless architectures is likely to lead to significant performance degradation, as frequent data sharing between different execution stages involves time-consuming remote storage access. Current platforms leverage memory cache to speed up remote access. However, conventional caching strategies show limited performance improvement. We experimentally find that the reason is that current strategies overlook the stage-dependent access patterns of stateful serverless applications, i.e., data that are read multiple times across stages (denoted as multi-read data) are wrongly evicted by data that are read only once (denoted as read-once data), causing a high cache miss ***, we propose a new caching strategy, Duo, whose design principle is to cache multi-read data as long as possible. Specifically, Duo contains a large cache list and a small cache list, which act as Leader list and Wingman list, respectively. Leader list ignores the data that is read for the first time to prevent itself from being polluted by massive read-once data at each stage. Wingman list inspects the data that are ignored or evicted by Leader list, and pre-fetches the data that will probably be read again based on the observation that multi-read data usually appear periodically in groups. Compared to the state-of-the-art works, Duo improves hit ratio by 1.1×-2.1× and reduces the data sharing overhead by 25%-62%.
Genome graphs analysis has emerged as an effective means to enable mapping DNA fragments (known as reads) to the reference genome. It replaces the traditional linear reference with a graph-based representation to augm...
Genome graphs analysis has emerged as an effective means to enable mapping DNA fragments (known as reads) to the reference genome. It replaces the traditional linear reference with a graph-based representation to augment the genetic variations and diversity information, significantly improving the quality of genotyping. The in-depth characterization of genome graphs analysis uncovers that it is bottlenecked by the irregular seed index access and the intensive alignment operation, stressing both the memory system and computing *** on these observations, we propose MeG 2 , a lightweight, commodity DRAM-compliant, processing-in-memory architecture to accelerate genome graphs analysis. MeG 2 is specifically integrated with the capabilities of both near-memory processing and bitwise in-situ computation. Specifically, MeG 2 leverages the low access latency of near-memory processing with the index-centric offload mechanism to alleviate the irregular memory access in the seeding procedure, and harnesses the row-parallel capacity of in-situ computation with the distance-aware technique to exploit the intensive computational parallelism in the alignment process. Results show that MeG 2 outperforms the CPU-, GPU-, and ASIC-based genome graphs analysis solutions by 502× (30.2×), 272× (15.1× ), and 5.5× (8.3×) for short (long) reads, while reducing energy consumption by 1628× (85.6×), 1443× (77.1×), and 7.8× (11.7×), respectively. We also demonstrate that MeG 2 offers significant improvements over existing PIM-based genome sequence analysis accelerators.
Optimal margin Distribution Machine (ODM) is a newly proposed statistical learning framework rooting in the latest margin theory, which demonstrates better generalization performance than the traditional large margin ...
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Cross-chain Decentralized Applications (dApps) are increasingly popular for their ability to handle complex tasks across various blockchains, extending beyond simple asset transfers or swaps. However, ensuring all dep...
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ISBN:
(数字)9798331530037
ISBN:
(纸本)9798331530044
Cross-chain Decentralized Applications (dApps) are increasingly popular for their ability to handle complex tasks across various blockchains, extending beyond simple asset transfers or swaps. However, ensuring all dependent transactions execute correctly together, known as complete atomicity, remains a challenge. Existing works provide financial atomicity, protecting against monetary loss, but lack the ability to ensure correctness for complex tasks. In this paper, we introduce Avalon, a transaction execution framework for cross-chain dApps that guarantees complete atomicity for the first time. Avalon achieves this by introducing multiple state layers above the native one to cache state transitions, allowing for efficient management of these state transitions. Most notably, for concurrent cross-chain transactions, Avalon resolves not only intra-chain conflicts but also addresses potential inconsistencies between blockchains via a novel state synchronization protocol, enabling serializable cross-chain execution. We implement Avalon using smart contracts in Cosmos ecosystem and evaluate its commitment performance, demonstrating acceptable latency and gas consumption even under conflict cases.
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