摘要：
The amount of energy needed to operate high-performance computing systems increases regularly since some years at a high pace, and the energy consumption has attracted a great deal of attention. Moreover, high energy consumption inevitably contains failures and reduces system reliability. However, there has been considerably less work of simultaneous management of system performance, reliability, and energy consumption on heterogeneous systems. In this paper, we first build the precedence-constrained parallel applications and energy consumption model. Then, we deduce the relation between reliability and processor frequencies and get their parameters approximation value by least squares curve fitting method. Thirdly, we establish a task execution reliability model and formulate this reliability and energy aware scheduling problem as a linear programming. Lastly, we propose a heuristic Reliability-Energy Aware Scheduling REAS algorithm to solve this problem, which can get good tradeoff among system performance, reliability, and energy consumption with lower complexity. Our extensive simulation performance evaluation study clearly demonstrates the tradeoff performance of our proposed heuristic algorithm. The amount of energy needed to operate high-performance computing systems increases regularly since some years at a high pace, and the energy consumption has attracted a great deal of attention. Moreover, high energy consumption inevitably contains failures and reduces system reliability. However, there has been considerably less work of simultaneous management of system performance, reliability, and energy consumption on heterogeneous systems. In this paper, we first build the precedence-constrained parallel applications and energy consumption model. Then, we deduce the relation between reliability and processor frequencies and get their parameters approximation value by least squares curve fitting method. Thirdly, we establish a task execution reliability model and formulate this reliability and energy aware scheduling problem as a linear programming. Lastly, we propose a heuristic Reliability-Energy Aware Scheduling REAS algorithm to solve this problem, which can get good tradeoff among system performance, reliability, and energy consumption with lower complexity. Our extensive simulation performance evaluation study clearly demonstrates the tradeoff performance of our proposed heuristic algorithm.
关键词：
Cache;Multi-core;task scheduling;schedule length;average response time
摘要：
In the past few years, multi-core processors incorporating four, six, eight, or more cores on a single die have become ubiquitous. Those cores, having their own private caches, often share a higher level cache memory, which leads to compete among different tasks. This can seriously affect the average performance of multi-core systems as the probability of cache hit could be lowered. In realizing this, we study the problem of scheduling bag-of-tasks (BoT) applications with shared cache constraint on multi-core systems. We first use cache space isolation techniques to divide shared caches into partitions. Then, we give a motivational example and outline the shared cache aware scheduling problem of multi-core systems. Finally, to provide an optimum solution for this problem, we propose a heuristic shared cache contention aware scheduling (SCAS) algorithm on multi-core systems. Our extensive simulation performance evaluation study clearly demonstrate that our proposed SCAS algorithm outperforms the existing traditional scheduling algorithm Min-min and the modified algorithm MSCAS in terms of schedule length and average response time.
摘要：
Multi-core systems, having their own private caches, often share a higher level cache memory, which leads to compete among different tasks. This can seriously affect the average performance of multi-core systems as the probability of cache hit could be lowered. In realizing this, we first use cache space isolation techniques to divide shared caches into partitions. To provide an optimum solution for this problem, we propose a heuristic shared cache contention aware scheduling (SCAS) algorithm on multi-core systems. Our extensive simulation performance evaluation study clearly demonstrate that our proposed SCAS algorithm outperforms the existing traditional scheduling algorithm Min-min and the modified algorithm MSCAS in terms of schedule length and average response time.
摘要：
The use of Cloud computing systems to run large-scale scientific, business and consumer based IT applications has increased rapidly in recent years. More and more Cloud users concern the data privacy protection and security in such systems. A natural way to tackle this problem is to adopt data encryption and access control policy. However, this solution is inevitably introduced a heavy computation overhead. In this paper, we first establish a trust model between Cloud servers and Cloud users. Then, we build the trust-aware attribute-based access control policies according to Cloud user trust level and Cloud request attributes. This technique can give different encryption and decryption data to Cloud user and substantive reduce the computation overhead of Cloud computing.
摘要：
Effective task scheduling is essential for obtaining high performance in heterogeneous computing systems (HCS). However, finding an effective task schedule in HCS, requires the consideration of the heterogeneity of computation and communication. To solve this problem, we present a list scheduling algorithm, called Heterogeneous Earliest Finish with Duplicator (HEFD). As task priority is a key attribute for list scheduling algorithm, this paper presents a new approach for computing their priority which considers the performance difference in target HCS using variance. Another novel idea proposed in this paper is to try to duplicate all parent tasks and get an optimal scheduling solution. The comparison study, based on both randomly generated graphs and the graphs of some real applications, shows that our scheduling algorithm HEFD significantly surpasses other three well-known algorithms.
作者机构：
[唐小勇] 湖南农业大学信息科学技术学院,长沙,410128;[唐小勇] 南大学计算机与通信学院,长沙,410082;Department of Computer Science University of Illinois at Urbana-Champaign,Champaign 61801,USA;[李肯立] 南大学计算机与通信学院;[PADUA Divid] 伊利诺伊大学
