Purpose-The overload effects associated with the use of mobile information and communication technologies (MICTs) in the workplace have become increasingly prevalent. The purpose of this paper is to examine the overload effects of using MICTs at work on employees' job satisfaction, and explore the corresponding coping strategies. Design/methodology/approach-The study is grounded on the cognitive load theory and the coping model of user adaptation. The overload antecedents and coping strategies are integrated into one model. Theoretical hypotheses are tested with survey data collected from a sample of 178 employees at work in China. Findings-The results indicate that information overload significantly reduces job satisfaction, while the influence of interruption overload on job satisfaction is not significant. Two coping strategies (information processing timeliness and job control assistant support) can significantly improve job satisfaction. Information processing timeliness significantly moderates the relationships between two types of overload effects and job satisfaction. Job control assistant support also significantly moderates the relationship between interruption overload and job satisfaction. Practical implications-This study suggests that information overload and interruption overload could constitute an important index to indicate employees' overload level when using MICTs at work. The two coping strategies provide managers with effective ways to improve employees' job satisfaction. By taking advantage of the moderation effects of coping strategies, managers could lower employees' evaluation of overload to an appropriate level. Originality/value-This study provides a comprehensive model to examine how the overload resulting from using MICTs in the workplace affects employees' work status, and how to cope with it. Two types of overload are conceptualized and corresponding coping strategies are identified. The measurements of principal constructs are developed and empirically validated. The results provide theoretical and practical insights on human resource management and human-computer interaction.

Abstract-As tenants take networked virtual machines (VMs) as their requirements, effective placement of VMs is needed to reduce the network cost in cloud data centers. The cost is one of the major concerns for the cloud providers. In addition to the cost caused by network traffics (N-cost), the cost caused by the utilization of physical machines (PM-cost) is also non-negligible. In this paper, we focus on the optimized placement of VMs to minimize the cost, the combination of N-cost and PM-cost. We define N-cost by various functions, according to different communication models. We formulate the placement problem, and prove it to be NP-hard. We investigate the problem from two aspects. Firstly, we put a special emphasis on minimizing the Ncost with fixed PM-cost. For the case that tenants request the same amount of VMs, we present optimal algorithms under various definitions of N-cost. For the case that tenants require different numbers of VMs, we propose an approximation algorithm. Also, a greedy algorithm is implemented as the baseline to evaluate the performance. Secondly, we study the general case of the VM placement problem, in which both N-cost and PM-cost are taken into account. We present an effective binary-searchbased algorithm to determine how many PMs should be used, which makes a tradeoff between PM-cost and N-cost. For all of the algorithms, we conduct theoretical analysis and extensive simulations to evaluate their performance and efficiency.

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