Electrical and Computer Engineering

The trend in architectural designs has been towards using simple cores for building multicore chips, instead of a single complex out-of-order (OOO) core, due to the increased complexity and energy requirements of out of order processors. Multicore chips provide better performance when compared with OOO cores while executing parallel applications. However, they are not able to exploit the parallelism inherent in single threaded applications. To this end, this paper presents a compiler optimization methodology coupled with minimal hardware extensions to extract simple fine-grained threads from a single-threaded application, for execution on multiple cores of a chip multiprocessor (CMP). These fine-grained threads are independent and eliminate the need for communication between cores, reducing costly communication latencies. This approach, which we call Parabilis is scalable for up to eight cores, and does not require complex hardware additions to simple multicore systems. Our evaluation shows that Parabilis yields an average speedup of 1.51 on an 8-core CMP architecture.

Cloud computing data centers are becoming increasingly popular for the provisioning of computing resources. The cost and operating expenses of data centers have skyrocketed with the increase in computing capacity. Several governmental, industrial, and academic surveys indicate that the energy utilized by computing and communication units within a data center contributes to a considerable slice of the data center operational costs. In this paper, we present a simulation environment for energy-aware cloud computing data centers. Along with the workload distribution, the simulator is designed to capture details of the energy consumed by data center components (servers, switches, and links) as well as packet-level communication patterns in realistic setups. The simulation results obtained for two-tier, three-tier, and three-tier high-speed data center architectures demonstrate the effectiveness of the simulator in utilizing power management schema, such as voltage scaling, frequency scaling, and dynamic shutdown that are applied to the computing and networking components.

In modern data centers, energy consumption accounts for a considerably large slice of operational expenses. The state of the art in data center energy optimization is focusing only on job distribution between computing servers based on workload or thermal profiles. This paper underlines the role of communication fabric in data center energy consumption and presents a scheduling approach that combines energy efficiency and network awareness, termed DENS. The DENS methodology balances the energy consumption of a data center, individual job performance, and traffic demands. The proposed approach optimizes the tradeoff between job consolidation (to minimize the amount of computing servers) and distribution of traffic patterns (to avoid hotspots in the data center network).

In modern parallel and distributed systems, inter-processor communications are a crucial factor of performance. The time for exchanging data is usually larger than that for computing elementary operations. Consequently, these communications slow down the execution of the application scheduled on the computing platform. Accounting for these communications is essential for attaining efficient hardware and software utilization. Moreover, energy dissipation due to the transfer of data between processing elements has become a major concern. Therefore, in this paper we develop an energy-aware static algorithm, which intrinsically optimizes the energy consumption due to the transfer of data in a distributed system. This is achieved by properly allocating and scheduling the tasks that constitute the applications on the processing elements, minimizing inter-processor communications. The proposed algorithm is a new Cellular Genetic Algorithm based on task clustering techniques. That is, the genetic operators work considering groups of tasks instead of applying them directly on the tasks. Simulation results showed that this algorithm is very compelling in terms of application completion time, inter-processor communication and energy communication dissipation.

Data object replication onto distributed servers can potentially alleviate bottlenecks, reduce network traffic, increase scalability, add robustness, and decrease user perceived access time. The decision of selecting data object and server pairs requires solving a constraint optimization problem that in general is NP-complete. In this paper, we abstract the distributed database system as an agent-based model, wherein agents continuously compete for allocation and reallocation of data objects. Each agent aims to replicate objects onto its server such that the communication cost is minimized. However, these agents do not have a global view of the system. Thereby, the optimization process becomes highly localized. Such localized optimization may severely affect the overall system performance. To cope with such localized optimization, we propose a “semi-distributed” axiomatic game theoretical mechanism. The mechanism’s control is unique in its decision making process, wherein all the heavy processing is done on the servers of the distributed system and the central body is only required to take a binary decision: (0) not to replicate or (1) to replicate. The cost model used by the agents in the mechanism for the purpose of identifying beneficial data objects is tailored made so that even though the agents take decisions based on their local knowledge domain, the effect is translated into a system-wide performance enhancement. The proposed mechanism is extensively compared against seven well-known conventional and three game theoretical replica allocation methods, namely, branch and bound, greedy, genetic, data-aware replication, tree inspired bottom-up procedure, tree inspired min-max procedure, Benders’ decomposition based procedure, game theoretical English auction, game theoretical Dutch auction, and game theoretical selfish replication procedure. The experimental setup incorporates GT-ITM, Inet network topology generators, Soccer World Cup 1998 access logs, and NASA Kennedy Space Center access logs to closely mimic the Web in its infrastructure and user access patterns. The experimental results reveal that the proposed technique despite its non-cooperative nature improves the solution quality and reduces the execution time compared to other techniques.