Many applications will require synchronized local clocks with varying levels of precision in order to maintain consistency and coordination in the network. While LoRaWan is heavily studied in applications and performance, the concept of time has rarely been characterized in such networks. Low-Power Wide Area Networks, such as LoRaWAN, are rapidly gaining popularity in the field of wireless sensing and actuation.
I further consider jitter’s conceptual affordances for media studies generally. Drawing on technical engineering literature and an ethnography of Los Angeles- based recording professionals, I articulate a broader sociotechnical definition of jitter and clocking, which I use to analyze three sites of temporal negotiation in the recording process: (1) the organization of clock signals in the analog-to-digital conversion process (2) the production of the studio as a heterochrony or “other time,” distinct from the world outside the studio and (3) the reconciliation of human and nonhuman temporalities, exemplified in the interaction between a drummer and a drum machine. Jitter is, in turn, managed through practices known as clocking. When clocks fall out of sync with one another, the result is a type of noise that signal- processing engineers call jitter. Second, they facilitate technologically mediated auditory communication. First, they create shared senses of temporality.
« lessĬlocks function as media objects in at least two ways. Our proposed algorithms are evaluated using power iteration applications on Amazon EC2. Under this framework, we propose optimal solutions of USEC systems with or without straggler tolerance using different storage placements. In this paper, we introduce a new optimization framework on Uncoded Storage Elastic Computing (USEC) more » systems with heterogeneous computing speed to minimize the overall computation time. In addition, based on our own measurement, virtual machines on Amazon EC2 clusters often have heterogeneous computation speed even if they have exactly the same configurations (e.g., CPU, RAM, I/O cost). Hence, it may be preferred to use uncoded storage by directly copying data into the virtual machines. However, one of the limitations of the CSEC is that it may only be applied to certain types of computations (e.g., linear) and may be challenging to be applied to more involved computations because the coded data storage and approximation are often needed.
in 2018 is an effective and efficient approach to overcome the elasticity and it costs relatively less storage and computation load. Coded Storage Elastic Computing (CSEC) introduced by Yang et al. Such elasticity means that virtual machines over the cloud can be preempted under a short notice (e.g., hours or minutes) if a high-priority job appears on the other hand, new virtual machines may become available over time to compensate the computing resources. Elasticity is one important feature in modern cloud computing systems and can result in computation failure or significantly increase computing time.
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