In recent years, by increasing CPU and I/O devices demands, running multiple tasks simultaneously becomes a crucial issue. This paper presents a new task scheduling algorithm for multi-CPU and multi-Hard Disk Drive (HDD) in soft Real-Time (RT) systems, which reduces the number of missed tasks. The aim of this paper is to execute more parallel tasks by considering an efficient trade-off between energy consumption and total execution time. For study purposes, we analyzed the proposed scheduling algorithm, named HCS (Hard disk drive and CPU Scheduling) in terms of the task set utilization, the total execution time, the average waiting time and the number of missed tasks from their deadlines. The results show that HCS algorithm improves the above mentioned criteria compared to the HCS_UE (Hard disk drive and CPU Scheduling _Unchanged Execution time) algorithm.

Cloud computing has proposed a new perspective for provisioning the large-scale computing resources by using virtualization technology and a pay-per-use cost model. Load balancing is taken into account as a vital part for parallel and distributed systems. It helps cloud computing systems by improving the general performance, better computing resources utilization, energy consumption management, enhancing the cloud services' QoS, avoiding SLA violation and maintaining system stability through distribution, controlling and managing the system workloads. In this paper we study the necessary requirements and considerations for designing and implementing a suitable load balancer for cloud environments. In addition we represent a complete survey of current proposed cloud load balancing solutions which according to our classification, they can be classified into three categories: General Algorithm-based, Architectural-based and Artificial Intelligence-based load balancing mechanisms. Finally, we propose our evaluation of these solutions based on suitable metrics and discuss their pros and cons.

In recent years, with an increasing number of requests, energy, power and temperature have been important keys in embedded systems, which decrease the lifetime of both CPUs and hard disks. The energy consumption is an important issue in computer systems, particularly real-time embedded systems. The frequency and the Revolutions Per Minute are major factors in the reduction of energy consumption in both processors and hard disk drives. Therefore, the main goal of this paper is to present a scheduling mechanism for a real time periodic task that can save more energy. This mechanism is based on increasing, as much as possible, the execution time of the CPU and/or the Read/Write time of the hard disk without passing the task deadline. This will be done by dynamically changing the CPU frequency and/or the RPM of hard disk. Our experimental results demonstrate that the proposed algorithm manages to lower energy consumption by an average of 25% and to reduce the number of missed tasks by 80%.

Distributed systems consist of several management sites which have different resource sharing levels. Resources can be shared among inner site and outer site processes at first and second level respectively. Global coordinator should exist in order to coordinate access to multi site’s shared resources. Moreover; some other coordinators should manage access to inner site’s shared resources so that exerting appropriate coordinator election algorithms in each level is crucial to achieve most efficient system. In this paper a hierarchical distributed election algorithm is proposed which eliminates single point of failure of election launcher. Meanwhile traffic is applied to network at different times and the number of election messages is extremely decreased as well which applies more efficiency especially in high traffic networks. A standby system between coordinators and their first alternative is considered to induct less wait time to processes which want to communicate with coordinator.

In data grids, data replication on variant nodes can change some problems such as response time and availability. Also, in data replication, there are some challenges to finding the best replica efficiently in relation to performance and location of physical storage systems. In this paper, various replica placement strategies are discussed. These replica placement strategies are available in the works. Replica placement contains recognizing the best possible node to duplicate data based on network latency and user request. These strategies measure and analyze different parameters such as access cost, bandwidth consumption, scalability, execution time and storage consumption. This paper also analyses the performance of various strategies with respect to the parameters mentioned above in data grid.

Nowadays use of distributed systems such as internet and cloud computing is growing dramatically. Coordinator existence in these systems is crucial due to processes coordinating and consistency requirement as well. However the growth makes their election algorithm even more complicated. Too many algorithms are proposed in this area but the two most well known one are Bully and Ring. In this paper we propose a fault tolerant coordinator election algorithm in typical bidirectional ring topology which is twice as fast as Ring algorithm although far fewer messages are passing due to election. Fault tolerance technique is applied which leads the waiting time for the election reaching to zero.